In the signal processing arena, it is generally accepted that the airways is not a perfect medium. Thus, it is usually taken for granted that when a signal is sent from a source node to a destination node, some noise and distortion will be injected into the source signal. Because noise and distortion are almost invariably introduced, a primary goal of signal processing is to compensate for, or to offset, the noise and distortion so that an exact replica (or a reasonably close replica) of the source signal can be reconstructed. To accomplish this goal, filters and equalizers are often implemented at the destination node. It is often the case that the distortion caused by a communications channel is not constant but rather varies over time. To compensate for this varying distortion, adaptive filters are typically used. In adaptive filters, the tap weights or coefficients of the filter are continuously updated to compensate for the varying amounts of distortion. Several types of adaptive filters are currently known in the art, including transversal adaptive filters. The adaptive filters currently known are effective when applied in situations where all portions of a signal are captured by a single receiver.
While in many applications, a signal can be captured and processed by a single receiver, there are applications in which a signal is only partially captured by each of a plurality of receivers. All of the receivers combined capture the entire signal but each individual receiver receives only a portion of the signal. An example of such a situation is one in which a source signal has a bandwidth of 40 MHz and the signal is captured (with some frequency overlap) by two receivers, each receiver having a receiving bandwidth of 22 MHz to capture approximately one half of the signal. The multichannel processing of wide band signals involves different techniques and considerations than for single-channel processing of signals.
Currently, two well known techniques are used for multichannel processing. The first technique involves the use of a multichannel adaptive filter. In this technique, different portions of the source signal are received by different receivers and the outputs of these receivers are summed to provide a reconstructed signal. This reconstructed signal is then passed through an adaptive filter to remove at least some of the distortion imposed by the various receivers or channels. The signal at the output of the filter is compared to a reference signal to derive an error component, and using this error component, the tap weights in the adaptive filter are modified so as to reduce the error component. This technique is adequate for some applications, but it has a significant drawback in that it is not robust. If the outputs of the various receivers are somehow time-delayed with respect to each other (as they may be if the receivers are separated from each other by a large distance), or if any of the receiver outputs are offset in frequency even by a small amount, this method cannot reconstruct a reasonable replica of the original source signal. Because this technique is relatively intolerant to equipment and implementation variations, its utility is limited.
An alternative technique for processing multichannel signals involves the use of quadrature mirror filters. According to this technique, the outputs of the various receivers are carefully filtered using quadrature mirror filters, thereby ensuring that the signal component from each receiver may be upsampled and added to the other to reconstruct the original signal. The filters are not adaptive. Such a technique is described in Crochiere and Rabiner, Multirate Signal Processing, Prentice-Hall, 1983. This technique has the same drawbacks as the one discussed above. Specifically, if the outputs of the receivers are time delayed with respect to one another in an unpredictable or variable manner, or if the outputs of the receivers have experienced an unknown offset in frequency, this technique cannot successfully reconstruct the original signal.
As discussed above, the techniques currently used for processing multichannel signals do not provide satisfactory results. A more robust method and system capable of tolerating variable and larger time delays, frequency offsets, and variable frequency overlaps is needed.